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Statistical estimates from black non-Hispanic female breast cancer data
Hafiz Mohammad Rafiqullah Khan1, Boubakari Ibrahimou, Anshul Saxena
1Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, USA
Asian Pacific Journal of Cancer Prevention : APJCP
|October 24, 2014
Summary
This study developed a Bayesian statistical model to predict breast cancer survival in Black non-Hispanic females. The exponentiated Weibull model demonstrated the best fit for survival time predictions.
Area of Science:
- Biostatistics
- Epidemiology
- Oncology
Background:
- Statistical methods are crucial for analyzing breast cancer survival data.
- Predictive modeling is essential for understanding disease progression and outcomes.
- Black non-Hispanic females face unique challenges in breast cancer survival.
Purpose of the Study:
- To develop the optimal statistical probability model using Bayesian methods.
- To predict future survival times for Black non-Hispanic female breast cancer patients.
- To analyze breast cancer survival data from 1973-2009 in the U.S.
Main Methods:
- Utilized a stratified random sample from the Surveillance Epidemiology and End Results (SEER) database.
- Employed Kaplan-Meier and Cox proportional regression for survival analysis.
- Compared four advanced statistical models: Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW), and Beta Inverse Weibull (BIW).
- Applied Bayesian approach for predictive survival inferences using the best-fit model.
Main Results:
- Identified geographical distribution of cases, with Michigan having the highest incidence and Hawaii the lowest.
- Mean age at diagnosis was 58.3 (SD: 14.43) years, and mean survival time was 66.8 (SD: 30.20) months.
- Non-Hispanic Black women showed a significantly higher risk of death (Hazard Ratio: 1.96) compared to Black Hispanic women.
- The exponentiated Weibull model provided the best fit for survival time data.
Conclusions:
- Findings are significant for treatment planning and healthcare cost allocation.
- The developed Bayesian approach can be applied to predict survival for other health-related diseases.
- This predictive model offers valuable insights for improving outcomes in Black non-Hispanic female breast cancer patients.
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